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A Hybrid Approach for Recommendation System in Web Graph Mining

by Priyanka U. Chavan, P. M. Yawalkar, D. V. Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 24
Year of Publication: 2014
Authors: Priyanka U. Chavan, P. M. Yawalkar, D. V. Patil

Priyanka U. Chavan, P. M. Yawalkar, D. V. Patil . A Hybrid Approach for Recommendation System in Web Graph Mining. International Journal of Computer Applications. 95, 24 ( June 2014), 23-27. DOI=10.5120/16743-7003

@article{ 10.5120/16743-7003,
author = { Priyanka U. Chavan, P. M. Yawalkar, D. V. Patil },
title = { A Hybrid Approach for Recommendation System in Web Graph Mining },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 24 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { },
doi = { 10.5120/16743-7003 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:20:18.853827+05:30
%A Priyanka U. Chavan
%A P. M. Yawalkar
%A D. V. Patil
%T A Hybrid Approach for Recommendation System in Web Graph Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 24
%P 23-27
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Large amount of information is available on web and information extraction takes place in huge volume. When queries are submitted to search engines they are generally in natural languages and contain just one or two words. Search engine are unable to recognize natural language and thus it becomes difficult to extract the proper information from web according to user's interest. Here, the recommendation technique comes into picture. There are number of recommendation techniques, every technique has its advantages and disadvantages. Recommendation techniques are designed in such a way that they support various type or data sources. These data sources are in the form of text, images, audio, video etc. Efficient way to deal with all types of data sources is, model them in the form of graph and then apply recommendation algorithm on it. Initially the proposed system collects data from user's and graphs are constructed by using this data. Subsequently the system uses different algorithms for predicting user's interest. The algorithms are item to item base collaborative filtering algorithm, Pearson correlation base collaborative filtering algorithm. These are applied for finding similarities between item and users respectively. Slope one algorithm is used to find out the rating of un-rated items. In proposed hybrid method results of these algorithms are combined. The hybridization of Algorithms leads to efficient results.

  1. G. Linden, B. smith, and J. York, O. Young, "Amazon. com Recommendations: item-to-item Collaborative filtering," IEEE internet computing, vol. 7, no. 1, pp. 76-80, Jan /feb. 2003.
  2. J. S. Breese, D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proc. 14th Conf. Uncertainty in Artificial Intelligence (UAI), 1998.
  3. A. s. Das, M. Datar, A. Garg and S. Rajaram, "Google News Personalization: Scalable Online Collaborative Filtering," WWW'07:Proc 16th International coneference on World wide web, pp. 271-280,2007.
  4. L. von Ahn and L. Dabbish,"Labeling Images with a Computer Game," CHI '04: Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 319-326, 2004.
  5. G. Pass, A. Chowdhury, and C. Torgeson, "A picture of search", In The First International Conference on Scalable Information Systems Kong,Hong Kong, June 2006.
  6. Y. -H. Yang, P. -T. Wu, C. -W. Lee, K. -H. Lin, W. H. Hsu, and H. Chen, "ContextSeer: Context Search and Recommendation at Query Time for Shared Consumer Photos," Proc. 16th ACM Int'l Conf. Multimedia, pp. 199-208, 2008.
  7. Robin van Meteren and Maarten van Someren. "Using Content-Based Filtering for Recommendation" . NetlinQ Group, Gerard Brandtstraat Amsterdam, 2010.
  8. Hao Ma, Irwin King and Michael R. Lyu, "Mining Web Graphs for Recommendations", IEEE transaction on knowledge and data engineering, 2012.
  9. Danil Nemirovsky "Web Graph and PageRank algorithm," Department of Technology of Programming, Faculty of Applied Mathematics and Control Processes, St. Petersburg State University,Russia,2009.
  10. M. Deshpande and G. Karypis, "Item-Based Top-n Recommendation," ACM Trans. Information Systems, vol. 22, no. 1, pp. 143-177, 2004.
  11. Hui Xiong, Shashi Shekhar, Pang­Ning Tan and Vipin Kumar, "Exploiting A Support­based Upper Bound of Pearson's Correlation Coef_cient for Ef_ciently Identifying Strongly Correlated Pairs," KDD'04, August 22. 25, 2004, Seattle, Washington, USA.
  12. Tongqiang JIANG and Wei LU, "Improved Slope One Algorithm Based On Time Weight," Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
Index Terms

Computer Science
Information Sciences


Recommendation system Web mining web graph personalization feature.